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In this paper, we meticulously examine the robustness of computer vision object detection frameworks within the intricate realm of real-world traffic scenarios, with a particular emphasis on challenging adverse weather conditions. Conventional evaluation methods often prove inadequate in addressing the complexities inherent in dynamic traffic environments—an increasingly vital consideration as global advancements in autonomous vehicle technologies persist. Our investigation delves specifically into the nuanced performance of these algorithms amidst adverse weather conditions like fog, rain, snow, sun flare, and more, acknowledging the substantial impact of weather dynamics on their precision. Significantly, we seek to underscore that an object detection framework excelling in clear weather may encounter significant challenges in adverse conditions. Our study incorporates in-depth ablation studies on dual modality architectures, exploring a range of applications including traffic monitoring, vehicle tracking, and object tracking. The ultimate goal is to elevate the safety and efficiency of transportation systems, recognizing the pivotal role of robust computer vision systems in shaping the trajectory of future autonomous and intelligent transportation technologies. 

Abstract Removing photobombing elements from images is a challenging task that requires sophisticated image inpainting techniques. Despite the availability of various methods, their effectiveness depends on the complexity of the image and the nature of the distracting element. To address this issue, we conducted a benchmark study to evaluate 10 state-of-the-art photobombing removal methods on a dataset of over 300 images. Our study focused on identifying the most effective image inpainting techniques for removing unwanted regions from images. We annotated the photobombed regions that require removal and evaluated the performance of each method using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). The results show that image inpainting techniques can effectively remove photobombing elements, but more robust and accurate methods are needed to handle various image complexities. Our benchmarking study provides a valuable resource for researchers and practitioners to select the most suitable method for their specific photobombing removal task.